Update README.md

README.md

@@ -3,6 +3,8 @@ license: mit
 ---
 [![Discord](https://img.shields.io/discord/232596713892872193?logo=discord)](https://discord.gg/2JhHVh7CGu)
 
+This is a severely undertrained research network as a POC for the architecture. It was trained on ~700 example images for 2000 epochs reaching a minimal MSE loss of ~0.06. This repo is meant only as a demo of a strong, <100M parameter example model that can achieve strong color balance and achieve low loss on pixel diffusion. The next step is scaling up the data.
+
 A semi custom network based on the follow paper [Simpler Diffusion (SiD2)](https://arxiv.org/abs/2410.19324v1)
 
 This network uses the optimal transport flow matching objective outlined [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747)
@@ -10,8 +12,9 @@ This network uses the optimal transport flow matching objective outlined [Flow M
 xATGLU Layers are used instead of linears for entry into the transformer MLP layer [Expanded Gating Ranges
 Improve Activation Functions](https://arxiv.org/pdf/2405.20768)
 
-`python train.py` will train a new image network on the provided dataset. A pretrained model is provided here, step_1799.safetensors. To test, use:
-`python test_sample.py step_1799.safetensors`
+```python train.py``` will train a new image network on the provided dataset. 
+
+```python test_sample.py step_1799.safetensors``` Where step_1799.safetensors is the desired model to test inference on. This will always generate a sample grid of 16x16 images.
 
 ![samples](./1.png)
 ![samples](./2.png)